no code implementations • 16 Apr 2024 • Brian Lai, Dennis S. Bernstein
KFLS is an extension of generalized forgetting recursive least squares (GF-RLS), a general framework which contains various extensions of RLS from the literature as special cases.
no code implementations • 16 Apr 2024 • Brian Lai, Dennis S. Bernstein
Traditionally, batch least squares (BLS) and recursive least squares (RLS) are used for identification of a vector of parameters that form a linear model.
no code implementations • 16 Apr 2024 • Brian Lai, Dennis S. Bernstein
Next, this work analyzes online identification of input/output models in the case where the order of the identified model is higher than that of the true system.
no code implementations • 16 Apr 2024 • Brian Lai, Dennis S. Bernstein
This paper presents subspace of information forgetting recursive least squares (SIFt-RLS), a directional forgetting algorithm which, at each step, forgets only in row space of the regressor matrix, or the \textit{information subspace}.
no code implementations • 28 Sep 2023 • Shashank Verma, Brian Lai, Dennis S. Bernstein
Digital PID control requires a differencing operation to implement the D gain.
no code implementations • 8 Aug 2023 • Brian Lai, Dennis S. Bernstein
This work presents generalized forgetting recursive least squares (GF-RLS), a generalization of recursive least squares (RLS) that encompasses many extensions of RLS as special cases.
no code implementations • 16 Jun 2021 • Brian Lai, Torbjørn Cunis, Laurent Burlion
A piecewise polynomial model of the GTM is used to simulate trajectories and the developed analysis tools are used to estimate the ROA around a trim condition based only on this trajectory data.
no code implementations • 16 Jun 2021 • Brian Lai, Syed Aseem Ul Islam, Dennis S. Bernstein
Within the context of recursive least squares (RLS) parameter estimation, the goal of the present paper is to study the effect of regularization-induced bias on the transient and asymptotic accuracy of the parameter estimates.
no code implementations • CVPR 2021 • Yanchao Yang, Brian Lai, Stefano Soatto
Then, it uses the segments to learn object models that can be used for detection in a static image.
no code implementations • 10 Oct 2019 • Nima Tajbakhsh, Brian Lai, Shilpa Ananth, Xiaowei Ding
In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.